Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations1.133
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory226.0 KiB
Average record size in memory204.2 B

Variable types

Categorical3
Numeric11

Alerts

deaths is highly overall correlated with match_time_sHigh correlation
kills is highly overall correlated with mvps and 1 other fieldsHigh correlation
match_time_s is highly overall correlated with deaths and 1 other fieldsHigh correlation
month is highly overall correlated with yearHigh correlation
mvps is highly overall correlated with kills and 1 other fieldsHigh correlation
ping is highly overall correlated with wait_time_sHigh correlation
points is highly overall correlated with kills and 2 other fieldsHigh correlation
wait_time_s is highly overall correlated with pingHigh correlation
year is highly overall correlated with monthHigh correlation
wait_time_s has 13 (1.1%) zeros Zeros
ping has 78 (6.9%) zeros Zeros
assists has 43 (3.8%) zeros Zeros
mvps has 228 (20.1%) zeros Zeros
hs_percent has 52 (4.6%) zeros Zeros

Reproduction

Analysis started2024-11-20 15:21:35.280768
Analysis finished2024-11-20 15:21:46.761711
Duration11.48 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

map
Categorical

Distinct10
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size61.7 KiB
Dust II
547 
Mirage
363 
Inferno
132 
Cache
59 
Overpass
 
13
Other values (5)
 
19

Length

Max length11
Median length7
Mean length6.6160635
Min length4

Characters and Unicode

Total characters7.496
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st rowMirage
2nd rowMirage
3rd rowMirage
4th rowMirage
5th rowMirage

Common Values

ValueCountFrequency (%)
Dust II 547
48.3%
Mirage 363
32.0%
Inferno 132
 
11.7%
Cache 59
 
5.2%
Overpass 13
 
1.1%
Cobblestone 12
 
1.1%
Nuke 4
 
0.4%
Austria 1
 
0.1%
Canals 1
 
0.1%
Italy 1
 
0.1%

Length

2024-11-20T22:21:46.863035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T22:21:46.978655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
dust 547
32.6%
ii 547
32.6%
mirage 363
21.6%
inferno 132
 
7.9%
cache 59
 
3.5%
overpass 13
 
0.8%
cobblestone 12
 
0.7%
nuke 4
 
0.2%
austria 1
 
0.1%
canals 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
I 1227
16.4%
e 595
 
7.9%
s 587
 
7.8%
t 561
 
7.5%
u 552
 
7.4%
D 547
 
7.3%
547
 
7.3%
r 509
 
6.8%
a 439
 
5.9%
i 364
 
4.9%
Other values (17) 1568
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 1227
16.4%
e 595
 
7.9%
s 587
 
7.8%
t 561
 
7.5%
u 552
 
7.4%
D 547
 
7.3%
547
 
7.3%
r 509
 
6.8%
a 439
 
5.9%
i 364
 
4.9%
Other values (17) 1568
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 1227
16.4%
e 595
 
7.9%
s 587
 
7.8%
t 561
 
7.5%
u 552
 
7.4%
D 547
 
7.3%
547
 
7.3%
r 509
 
6.8%
a 439
 
5.9%
i 364
 
4.9%
Other values (17) 1568
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 1227
16.4%
e 595
 
7.9%
s 587
 
7.8%
t 561
 
7.5%
u 552
 
7.4%
D 547
 
7.3%
547
 
7.3%
r 509
 
6.8%
a 439
 
5.9%
i 364
 
4.9%
Other values (17) 1568
20.9%

day
Real number (ℝ)

Distinct31
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.598411
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-11-20T22:21:47.088052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median18
Q324
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7119327
Coefficient of variation (CV)0.52486545
Kurtosis-1.1487446
Mean16.598411
Median Absolute Deviation (MAD)7
Skewness-0.13596885
Sum18806
Variance75.897771
MonotonicityNot monotonic
2024-11-20T22:21:47.202271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
22 52
 
4.6%
20 50
 
4.4%
19 49
 
4.3%
23 48
 
4.2%
27 47
 
4.1%
28 46
 
4.1%
1 45
 
4.0%
12 43
 
3.8%
9 42
 
3.7%
18 42
 
3.7%
Other values (21) 669
59.0%
ValueCountFrequency (%)
1 45
4.0%
2 27
2.4%
3 20
1.8%
4 19
1.7%
5 35
3.1%
6 39
3.4%
7 38
3.4%
8 37
3.3%
9 42
3.7%
10 27
2.4%
ValueCountFrequency (%)
31 34
3.0%
30 28
2.5%
29 28
2.5%
28 46
4.1%
27 47
4.1%
26 38
3.4%
25 36
3.2%
24 40
3.5%
23 48
4.2%
22 52
4.6%

month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0697264
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-11-20T22:21:47.290377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3021424
Coefficient of variation (CV)0.46708207
Kurtosis-1.1116133
Mean7.0697264
Median Absolute Deviation (MAD)3
Skewness-0.1718291
Sum8010
Variance10.904145
MonotonicityNot monotonic
2024-11-20T22:21:47.386635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 128
11.3%
6 122
10.8%
11 115
10.2%
12 109
9.6%
9 105
9.3%
10 102
9.0%
4 99
8.7%
5 80
7.1%
2 77
6.8%
3 72
6.4%
Other values (2) 124
10.9%
ValueCountFrequency (%)
1 53
4.7%
2 77
6.8%
3 72
6.4%
4 99
8.7%
5 80
7.1%
6 122
10.8%
7 71
6.3%
8 128
11.3%
9 105
9.3%
10 102
9.0%
ValueCountFrequency (%)
12 109
9.6%
11 115
10.2%
10 102
9.0%
9 105
9.3%
8 128
11.3%
7 71
6.3%
6 122
10.8%
5 80
7.1%
4 99
8.7%
3 72
6.4%

year
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size61.0 KiB
2017.0
449 
2015.0
408 
2018.0
167 
2016.0
109 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6.798
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018.0
2nd row2018.0
3rd row2018.0
4th row2018.0
5th row2018.0

Common Values

ValueCountFrequency (%)
2017.0 449
39.6%
2015.0 408
36.0%
2018.0 167
 
14.7%
2016.0 109
 
9.6%

Length

2024-11-20T22:21:47.470015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T22:21:47.552639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2017.0 449
39.6%
2015.0 408
36.0%
2018.0 167
 
14.7%
2016.0 109
 
9.6%

Most occurring characters

ValueCountFrequency (%)
0 2266
33.3%
2 1133
16.7%
1 1133
16.7%
. 1133
16.7%
7 449
 
6.6%
5 408
 
6.0%
8 167
 
2.5%
6 109
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2266
33.3%
2 1133
16.7%
1 1133
16.7%
. 1133
16.7%
7 449
 
6.6%
5 408
 
6.0%
8 167
 
2.5%
6 109
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2266
33.3%
2 1133
16.7%
1 1133
16.7%
. 1133
16.7%
7 449
 
6.6%
5 408
 
6.0%
8 167
 
2.5%
6 109
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2266
33.3%
2 1133
16.7%
1 1133
16.7%
. 1133
16.7%
7 449
 
6.6%
5 408
 
6.0%
8 167
 
2.5%
6 109
 
1.6%

wait_time_s
Real number (ℝ)

High correlation  Zeros 

Distinct341
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.5331
Minimum0
Maximum589
Zeros13
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-11-20T22:21:47.654878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median48
Q3213
95-th percentile379.4
Maximum589
Range589
Interquartile range (IQR)204

Descriptive statistics

Standard deviation134.12968
Coefficient of variation (CV)1.13158
Kurtosis-0.014647036
Mean118.5331
Median Absolute Deviation (MAD)46
Skewness1.0337844
Sum134298
Variance17990.772
MonotonicityNot monotonic
2024-11-20T22:21:47.784420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 49
 
4.3%
1 48
 
4.2%
2 37
 
3.3%
5 32
 
2.8%
10 31
 
2.7%
4 27
 
2.4%
8 21
 
1.9%
6 21
 
1.9%
7 21
 
1.9%
11 17
 
1.5%
Other values (331) 829
73.2%
ValueCountFrequency (%)
0 13
 
1.1%
1 48
4.2%
2 37
3.3%
3 49
4.3%
4 27
2.4%
5 32
2.8%
6 21
1.9%
7 21
1.9%
8 21
1.9%
9 17
 
1.5%
ValueCountFrequency (%)
589 1
0.1%
571 1
0.1%
551 1
0.1%
541 1
0.1%
529 1
0.1%
521 1
0.1%
518 1
0.1%
510 1
0.1%
503 1
0.1%
500 1
0.1%

match_time_s
Real number (ℝ)

High correlation 

Distinct848
Distinct (%)74.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2291.4387
Minimum630
Maximum3603
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-11-20T22:21:47.900257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum630
5-th percentile1520.6
Q11942
median2311
Q32648
95-th percentile3025.6
Maximum3603
Range2973
Interquartile range (IQR)706

Descriptive statistics

Standard deviation474.52433
Coefficient of variation (CV)0.20708576
Kurtosis-0.27017807
Mean2291.4387
Median Absolute Deviation (MAD)355
Skewness-0.22528969
Sum2596200
Variance225173.34
MonotonicityNot monotonic
2024-11-20T22:21:48.003658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2459 4
 
0.4%
1906 4
 
0.4%
1951 4
 
0.4%
2266 4
 
0.4%
2472 4
 
0.4%
2443 4
 
0.4%
2340 3
 
0.3%
2728 3
 
0.3%
2445 3
 
0.3%
1659 3
 
0.3%
Other values (838) 1097
96.8%
ValueCountFrequency (%)
630 1
0.1%
668 1
0.1%
880 1
0.1%
922 1
0.1%
924 1
0.1%
937 1
0.1%
981 1
0.1%
983 1
0.1%
1023 1
0.1%
1116 1
0.1%
ValueCountFrequency (%)
3603 1
0.1%
3467 1
0.1%
3429 1
0.1%
3403 1
0.1%
3364 1
0.1%
3345 1
0.1%
3344 1
0.1%
3327 1
0.1%
3265 1
0.1%
3259 1
0.1%

ping
Real number (ℝ)

High correlation  Zeros 

Distinct187
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.494263
Minimum0
Maximum724
Zeros78
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-11-20T22:21:48.116787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median76
Q392
95-th percentile164
Maximum724
Range724
Interquartile range (IQR)61

Descriptive statistics

Standard deviation65.995966
Coefficient of variation (CV)0.91036122
Kurtosis20.064676
Mean72.494263
Median Absolute Deviation (MAD)36
Skewness3.34517
Sum82136
Variance4355.4675
MonotonicityNot monotonic
2024-11-20T22:21:48.240296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78
 
6.9%
90 39
 
3.4%
88 29
 
2.6%
85 28
 
2.5%
89 28
 
2.5%
92 27
 
2.4%
87 26
 
2.3%
39 25
 
2.2%
82 23
 
2.0%
40 23
 
2.0%
Other values (177) 807
71.2%
ValueCountFrequency (%)
0 78
6.9%
9 1
 
0.1%
10 1
 
0.1%
11 5
 
0.4%
12 4
 
0.4%
13 8
 
0.7%
14 11
 
1.0%
15 14
 
1.2%
16 11
 
1.0%
17 6
 
0.5%
ValueCountFrequency (%)
724 1
0.1%
632 1
0.1%
563 1
0.1%
449 1
0.1%
422 1
0.1%
390 1
0.1%
387 1
0.1%
384 2
0.2%
376 1
0.1%
363 1
0.1%

kills
Real number (ℝ)

High correlation 

Distinct36
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.70609
Minimum0
Maximum36
Zeros8
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-11-20T22:21:48.354645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q111
median14
Q318
95-th percentile25
Maximum36
Range36
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.9487749
Coefficient of variation (CV)0.40451098
Kurtosis0.21124152
Mean14.70609
Median Absolute Deviation (MAD)4
Skewness0.25112063
Sum16662
Variance35.387922
MonotonicityNot monotonic
2024-11-20T22:21:48.469916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
13 92
 
8.1%
15 78
 
6.9%
12 77
 
6.8%
17 74
 
6.5%
11 72
 
6.4%
16 71
 
6.3%
14 66
 
5.8%
10 64
 
5.6%
18 57
 
5.0%
19 54
 
4.8%
Other values (26) 428
37.8%
ValueCountFrequency (%)
0 8
 
0.7%
1 10
 
0.9%
2 4
 
0.4%
3 7
 
0.6%
4 7
 
0.6%
5 22
1.9%
6 26
2.3%
7 43
3.8%
8 28
2.5%
9 46
4.1%
ValueCountFrequency (%)
36 2
 
0.2%
34 2
 
0.2%
33 1
 
0.1%
32 1
 
0.1%
31 3
 
0.3%
30 3
 
0.3%
29 5
 
0.4%
28 6
 
0.5%
27 9
0.8%
26 16
1.4%

assists
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1588703
Minimum0
Maximum14
Zeros43
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-11-20T22:21:48.555941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile8
Maximum14
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3634816
Coefficient of variation (CV)0.56829895
Kurtosis0.68523804
Mean4.1588703
Median Absolute Deviation (MAD)2
Skewness0.6338052
Sum4712
Variance5.5860454
MonotonicityNot monotonic
2024-11-20T22:21:48.832928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
4 196
17.3%
3 179
15.8%
5 173
15.3%
2 151
13.3%
6 120
10.6%
1 98
8.6%
7 82
7.2%
8 44
 
3.9%
0 43
 
3.8%
9 18
 
1.6%
Other values (5) 29
 
2.6%
ValueCountFrequency (%)
0 43
 
3.8%
1 98
8.6%
2 151
13.3%
3 179
15.8%
4 196
17.3%
5 173
15.3%
6 120
10.6%
7 82
7.2%
8 44
 
3.9%
9 18
 
1.6%
ValueCountFrequency (%)
14 2
 
0.2%
13 2
 
0.2%
12 5
 
0.4%
11 4
 
0.4%
10 16
 
1.4%
9 18
 
1.6%
8 44
 
3.9%
7 82
7.2%
6 120
10.6%
5 173
15.3%

deaths
Real number (ℝ)

High correlation 

Distinct28
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.034422
Minimum0
Maximum27
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-11-20T22:21:48.934155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q114
median18
Q321
95-th percentile23
Maximum27
Range27
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.7924832
Coefficient of variation (CV)0.28134111
Kurtosis0.43894676
Mean17.034422
Median Absolute Deviation (MAD)3
Skewness-0.83456697
Sum19300
Variance22.967895
MonotonicityNot monotonic
2024-11-20T22:21:49.036066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
17 116
10.2%
20 109
 
9.6%
21 106
 
9.4%
18 101
 
8.9%
19 97
 
8.6%
22 95
 
8.4%
16 77
 
6.8%
15 50
 
4.4%
14 48
 
4.2%
23 48
 
4.2%
Other values (18) 286
25.2%
ValueCountFrequency (%)
0 2
 
0.2%
1 2
 
0.2%
2 3
 
0.3%
3 6
 
0.5%
4 7
 
0.6%
5 8
 
0.7%
6 8
 
0.7%
7 13
1.1%
8 25
2.2%
9 25
2.2%
ValueCountFrequency (%)
27 1
 
0.1%
26 7
 
0.6%
25 13
 
1.1%
24 23
 
2.0%
23 48
4.2%
22 95
8.4%
21 106
9.4%
20 109
9.6%
19 97
8.6%
18 101
8.9%

mvps
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8252427
Minimum0
Maximum8
Zeros228
Zeros (%)20.1%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-11-20T22:21:49.140563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4876993
Coefficient of variation (CV)0.81506929
Kurtosis0.66210619
Mean1.8252427
Median Absolute Deviation (MAD)1
Skewness0.86100925
Sum2068
Variance2.2132492
MonotonicityNot monotonic
2024-11-20T22:21:49.240664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 304
26.8%
2 282
24.9%
0 228
20.1%
3 169
14.9%
4 92
 
8.1%
5 32
 
2.8%
6 18
 
1.6%
7 7
 
0.6%
8 1
 
0.1%
ValueCountFrequency (%)
0 228
20.1%
1 304
26.8%
2 282
24.9%
3 169
14.9%
4 92
 
8.1%
5 32
 
2.8%
6 18
 
1.6%
7 7
 
0.6%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
7 7
 
0.6%
6 18
 
1.6%
5 32
 
2.8%
4 92
 
8.1%
3 169
14.9%
2 282
24.9%
1 304
26.8%
0 228
20.1%

hs_percent
Real number (ℝ)

Zeros 

Distinct61
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.90203
Minimum0
Maximum100
Zeros52
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-11-20T22:21:49.354746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q116
median23
Q333
95-th percentile50
Maximum100
Range100
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.672214
Coefficient of variation (CV)0.54904014
Kurtosis1.5098388
Mean24.90203
Median Absolute Deviation (MAD)8
Skewness0.70889188
Sum28214
Variance186.92944
MonotonicityNot monotonic
2024-11-20T22:21:49.467215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 57
 
5.0%
20 55
 
4.9%
23 52
 
4.6%
0 52
 
4.6%
33 49
 
4.3%
25 48
 
4.2%
30 48
 
4.2%
15 39
 
3.4%
27 38
 
3.4%
21 36
 
3.2%
Other values (51) 659
58.2%
ValueCountFrequency (%)
0 52
4.6%
3 2
 
0.2%
4 2
 
0.2%
5 9
 
0.8%
6 7
 
0.6%
7 19
 
1.7%
8 14
 
1.2%
9 21
1.9%
10 23
2.0%
11 30
2.6%
ValueCountFrequency (%)
100 2
 
0.2%
75 1
 
0.1%
71 1
 
0.1%
66 4
0.4%
64 1
 
0.1%
62 3
0.3%
61 2
 
0.2%
60 5
0.4%
58 1
 
0.1%
57 4
0.4%

points
Real number (ℝ)

High correlation 

Distinct84
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.217123
Minimum0
Maximum89
Zeros3
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-11-20T22:21:49.572291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q129
median39
Q349
95-th percentile64
Maximum89
Range89
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.898022
Coefficient of variation (CV)0.37988564
Kurtosis0.1743789
Mean39.217123
Median Absolute Deviation (MAD)10
Skewness0.10814162
Sum44433
Variance221.95105
MonotonicityNot monotonic
2024-11-20T22:21:49.683223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 37
 
3.3%
35 33
 
2.9%
37 33
 
2.9%
47 33
 
2.9%
33 32
 
2.8%
31 31
 
2.7%
44 31
 
2.7%
45 30
 
2.6%
42 30
 
2.6%
43 30
 
2.6%
Other values (74) 813
71.8%
ValueCountFrequency (%)
0 3
0.3%
1 4
0.4%
2 3
0.3%
3 5
0.4%
4 3
0.3%
5 2
 
0.2%
7 5
0.4%
8 1
 
0.1%
9 1
 
0.1%
10 4
0.4%
ValueCountFrequency (%)
89 2
0.2%
88 1
0.1%
83 2
0.2%
82 1
0.1%
81 1
0.1%
80 1
0.1%
78 1
0.1%
77 1
0.1%
76 1
0.1%
75 2
0.2%

result
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size58.2 KiB
Lost
560 
Win
488 
Tie
85 

Length

Max length4
Median length3
Mean length3.494263
Min length3

Characters and Unicode

Total characters3.959
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWin
2nd rowLost
3rd rowWin
4th rowLost
5th rowTie

Common Values

ValueCountFrequency (%)
Lost 560
49.4%
Win 488
43.1%
Tie 85
 
7.5%

Length

2024-11-20T22:21:49.805369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-20T22:21:49.887661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
lost 560
49.4%
win 488
43.1%
tie 85
 
7.5%

Most occurring characters

ValueCountFrequency (%)
i 573
14.5%
L 560
14.1%
o 560
14.1%
s 560
14.1%
t 560
14.1%
W 488
12.3%
n 488
12.3%
T 85
 
2.1%
e 85
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3959
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 573
14.5%
L 560
14.1%
o 560
14.1%
s 560
14.1%
t 560
14.1%
W 488
12.3%
n 488
12.3%
T 85
 
2.1%
e 85
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3959
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 573
14.5%
L 560
14.1%
o 560
14.1%
s 560
14.1%
t 560
14.1%
W 488
12.3%
n 488
12.3%
T 85
 
2.1%
e 85
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3959
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 573
14.5%
L 560
14.1%
o 560
14.1%
s 560
14.1%
t 560
14.1%
W 488
12.3%
n 488
12.3%
T 85
 
2.1%
e 85
 
2.1%

Interactions

2024-11-20T22:21:45.491096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:35.722478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:36.806342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:37.750824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:38.670866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:39.635030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:40.621893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:41.567947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:42.707233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:43.620602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:44.569770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:45.574730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:35.826935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:36.889125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:37.821814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:38.762638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:39.720135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:40.715388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:41.653659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:42.789199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:43.706534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:44.656091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:45.652266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:35.906821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:36.972085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:37.907863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:38.851511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:39.820517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:40.799055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:41.734106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:42.872984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:43.795646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:44.740552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:45.742259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:35.990616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:37.056191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:37.998135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:38.938065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:39.910244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:40.876529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:41.822273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:42.956940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:43.880465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:44.819738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:45.826583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:36.070018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:37.136951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:38.087292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:39.019773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:39.991710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:40.970052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:41.907357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:43.036393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:43.968349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:44.912552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:45.920295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:36.154693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:37.225866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:38.168633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:39.103196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:40.086608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:41.053774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:42.008761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:43.118130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:44.058704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:45.000290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:46.003235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:36.388621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:37.314605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:38.258876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:39.204611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:40.206563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:41.139480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:42.103076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:43.202999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:44.140707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:45.085896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:46.077940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:36.484369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:37.403332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:38.345751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:39.295407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:40.285057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:41.219893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:42.186441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:43.288695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:44.236344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:45.171223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:46.153762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:36.564861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:37.476318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:38.430548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:39.370046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:40.369417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:41.301448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:42.271225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:43.371186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:44.321715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:45.250951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:46.236337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:36.652757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:37.578300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:38.520999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:39.470649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:40.467314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:41.405513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:42.370412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:43.453062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:44.410325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:45.336987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:46.323063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:36.730243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:37.666893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:38.603959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:39.554056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:40.542780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:41.490457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:42.456399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:43.537221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:44.494312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-20T22:21:45.420425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-20T22:21:49.954095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
assistsdaydeathshs_percentkillsmapmatch_time_smonthmvpspingpointsresultwait_time_syear
assists1.000-0.0020.3110.0780.2750.1400.321-0.0350.2100.1240.4210.1210.0020.000
day-0.0021.000-0.0340.016-0.0690.032-0.009-0.095-0.0050.093-0.0520.0000.1290.178
deaths0.311-0.0341.0000.0820.2080.0280.6310.0150.0110.1270.2400.400-0.0120.044
hs_percent0.0780.0160.0821.000-0.0080.0000.0150.047-0.035-0.047-0.0050.088-0.0690.084
kills0.275-0.0690.208-0.0081.0000.0000.4430.0210.6120.1580.9450.2230.0530.079
map0.1400.0320.0280.0000.0001.0000.0000.1420.0000.0820.0000.0000.1050.295
match_time_s0.321-0.0090.6310.0150.4430.0001.000-0.0080.2360.0820.5020.3060.1090.093
month-0.035-0.0950.0150.0470.0210.142-0.0081.0000.027-0.2560.0210.050-0.3450.575
mvps0.210-0.0050.011-0.0350.6120.0000.2360.0271.0000.1190.6430.2460.0410.065
ping0.1240.0930.127-0.0470.1580.0820.082-0.2560.1191.0000.1390.0000.5350.483
points0.421-0.0520.240-0.0050.9450.0000.5020.0210.6430.1391.0000.2450.0350.086
result0.1210.0000.4000.0880.2230.0000.3060.0500.2460.0000.2451.0000.0220.032
wait_time_s0.0020.129-0.012-0.0690.0530.1050.109-0.3450.0410.5350.0350.0221.0000.479
year0.0000.1780.0440.0840.0790.2950.0930.5750.0650.4830.0860.0320.4791.000

Missing values

2024-11-20T22:21:46.465575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-20T22:21:46.642596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

mapdaymonthyearwait_time_smatch_time_spingkillsassistsdeathsmvpshs_percentpointsresult
0Mirage3.08.02018.0327.02906.0215.017.02.021.02.05.045.0Win
1Mirage2.08.02018.0336.02592.0199.013.04.024.02.00.040.0Lost
2Mirage31.07.02018.0414.02731.085.015.03.018.03.026.037.0Win
3Mirage31.07.02018.0317.02379.093.012.02.015.02.016.030.0Lost
4Mirage30.07.02018.0340.03467.094.033.05.020.05.030.083.0Tie
5Mirage29.07.02018.0391.01881.088.013.02.017.01.038.034.0Lost
6Dust II28.07.02018.0274.03194.089.019.05.024.02.015.052.0Lost
7Mirage27.07.02018.0291.02859.082.017.01.025.00.035.037.0Lost
8Mirage27.07.02018.0184.02300.0112.025.010.012.04.016.070.0Win
9Mirage27.07.02018.0375.02184.089.013.01.017.00.061.031.0Lost
mapdaymonthyearwait_time_smatch_time_spingkillsassistsdeathsmvpshs_percentpointsresult
1123Dust II25.07.02015.00.02029.024.012.03.017.01.025.029.0Lost
1124Dust II24.07.02015.041.02845.022.015.07.022.01.060.042.0Win
1125Dust II24.07.02015.05.02349.034.013.01.021.01.030.028.0Lost
1126Dust II24.07.02015.026.01834.023.06.03.015.00.050.017.0Win
1127Dust II23.07.02015.068.01819.099.010.05.012.01.030.030.0Win
1128Dust II23.07.02015.02.01573.046.012.02.020.00.016.026.0Lost
1129Dust II23.07.02015.029.02126.041.019.06.021.02.031.051.0Lost
1130Dust II23.07.02015.010.02555.017.09.03.020.02.055.030.0Lost
1131Dust II23.07.02015.09.02293.020.011.04.020.01.027.031.0Lost
1132Dust II23.07.02015.03.01858.016.06.03.015.01.016.023.0Win